TAlignDiff: Automatic Tooth Alignment assisted by Diffusion-based Transformation Learning

TL;DR

TAlignDiff is a novel automatic tooth alignment method that leverages diffusion-based transformation learning to better model the distribution of transformation matrices, improving accuracy over traditional geometric constraint methods.

Contribution

The paper introduces TAlignDiff, combining point cloud regression with diffusion-based transformation modeling for more effective tooth alignment.

Findings

Outperforms existing methods in accuracy and robustness.

Effectively captures the distribution of transformation matrices.

Demonstrates potential for clinical orthodontic applications.

Abstract

Orthodontic treatment hinges on tooth alignment, which significantly affects occlusal function, facial aesthetics, and patients' quality of life. Current deep learning approaches predominantly concentrate on predicting transformation matrices through imposing point-to-point geometric constraints for tooth alignment. Nevertheless, these matrices are likely associated with the anatomical structure of the human oral cavity and possess particular distribution characteristics that the deterministic point-to-point geometric constraints in prior work fail to capture. To address this, we introduce a new automatic tooth alignment method named TAlignDiff, which is supported by diffusion-based transformation learning. TAlignDiff comprises two main components: a primary point cloud-based regression network (PRN) and a diffusion-based transformation matrix denoising module (DTMD). Geometry-constrained losses supervise PRN learning for point cloud-level alignment. DTMD, as an auxiliary module, learns the latent distribution of transformation matrices from clinical data. We integrate point cloud-based transformation regression and diffusion-based transformation modeling into a unified framework, allowing bidirectional feedback between geometric constraints and diffusion refinement. Extensive ablation and comparative experiments demonstrate the effectiveness and superiority of our method, highlighting its potential in orthodontic treatment.